标题： 基于超声和临床数据的多模态深度学习用于叶状肿瘤分类 显示英文标题

标题： Multimodal Deep Learning for Phyllodes Tumor Classification from Ultrasound and Clinical Data

摘要： 叶状肿瘤（PTs）是罕见的纤维上皮乳腺病变，由于其影像学表现与良性纤维腺瘤相似，术前难以分类。这通常会导致不必要的手术切除。为了解决这个问题，我们提出了一种多模态深度学习框架，将乳腺超声（BUS）图像与结构化临床数据相结合，以提高诊断准确性。我们开发了一个双分支神经网络，从81名确诊PTs的受试者的超声图像和患者元数据中提取并融合特征。应用了类感知采样和受试者分层的5折交叉验证，以防止类别不平衡和数据泄露。结果表明，我们提出的多模态方法在分类良性与中间/恶性PTs方面优于单模态基线。在六个图像编码器中，ConvNeXt和ResNet18在多模态设置中表现最佳，AUC-ROC分数分别为0.9427和0.9349，F1分数分别为0.6720和0.7294。这项研究证明了多模态AI作为非侵入性诊断工具的潜力，可以减少不必要的活检，并改善乳腺肿瘤管理中的临床决策。 显示英文摘要

摘要： Phyllodes tumors (PTs) are rare fibroepithelial breast lesions that are difficult to classify preoperatively due to their radiological similarity to benign fibroadenomas. This often leads to unnecessary surgical excisions. To address this, we propose a multimodal deep learning framework that integrates breast ultrasound (BUS) images with structured clinical data to improve diagnostic accuracy. We developed a dual-branch neural network that extracts and fuses features from ultrasound images and patient metadata from 81 subjects with confirmed PTs. Class-aware sampling and subject-stratified 5-fold cross-validation were applied to prevent class imbalance and data leakage. The results show that our proposed multimodal method outperforms unimodal baselines in classifying benign versus borderline/malignant PTs. Among six image encoders, ConvNeXt and ResNet18 achieved the best performance in the multimodal setting, with AUC-ROC scores of 0.9427 and 0.9349, and F1-scores of 0.6720 and 0.7294, respectively. This study demonstrates the potential of multimodal AI to serve as a non-invasive diagnostic tool, reducing unnecessary biopsies and improving clinical decision-making in breast tumor management.